8 INTRODUCTION TO STATISTICS FOR BIOMEDICAL ENGINEERS
MC: Ropella Ch02_Page 8 - 09/26/2007, 11:51AM Achorn International
(before and after) that may result because we are using two different sets of experimental units. For
example, if we used one set of subjects before therapy and then an entirely different set of subjects
after therapy (Figure 2.1, right), there is a chance that the two sets of subjects may vary enough in
sex, age, weight, race, or genetic makeup, which would lead to a difference in response to the therapy
that has little to do with the underlying therapy. In other words, there may be confounding factors
that contribute to the difference in the experimental outcome before and after therapy that are not
only a factor of the therapy but really an artifact of differences in the distributions of the two differ
-
ent groups of subjects from which the two samples sets were chosen. Blocking will help to eliminate
the effect of intersubject variability.
However, blocking is not always possible, given the nature of some biomedical research stud
-
ies. For example, if one wanted to study the effectiveness of two different chemotherapy drugs in
reducing tumor size, it is impractical to test both drugs on the same tumor mass. Thus, the two
drugs are tested on different groups of individuals. The same type of design would be necessary for
testing the effectiveness of weight-loss regimens.
Thus, some important concepts and definitions to keep in mind when designing experiments
include the following:
experimental unit: the item, object, or subject to which we apply the treatment and from
which we take sample measurements;
randomization: allocate the treatments randomly to the experimental units;
blocking: assigning all treatments within a factor to every level of the blocking factor.
Often, the blocking factor is the experimental unit. Note that in using blocking, we still
randomize the order in which treatments are applied to each experimental unit to avoid
ordering bias.
Finally, the experimentalist must always think about how representative the sample population is
with respect to the greater underlying population. Because it is virtually impossible to test every
member of a population or every product rolling down an assembly line, especially when destruc
-
tive testing methods are used, the biomedical engineer must often collect data from a much smaller
sample drawn from the larger population. It is important, if the statistics are going to lead to useful
conclusions, that the sample population captures the variability of the underlying population. What
is even more challenging is that we often do not have a good grasp of the variability of the underly
-
ing population, and because of expense and respect for life, we are typically limited in the number of
samples we may collect in biomedical research and manufacturing. These limitations are not easy to
address and require that the engineer always consider how fair the sample and data analysis is and
how well it represents the underlying population(s) from which the samples are drawn.
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